A Beginning for Mathematics: A Professor's Positive Vision for the AI Era

A Beginning for Mathematics

Daniel Litt, a math professor at the University of Toronto, shifts from his earlier 'End of Mathematics' talk to a positive vision. He assumes AI will soon be superhuman at most math tasks. The core issue isn't AI solving problems—it's how humans keep producing understanding. He argues that protecting old institutions like journals and peer review is futile when high-quality results cost a few dollars to generate. Instead, he proposes preserving what actually builds human understanding: learning seminars, serendipitous conversations, and students dropping by to talk math. The post does not lay out concrete reform steps, but explicitly rejects chasing the edge of model capabilities and urges planning for the endgame directly.

Why it matters: Daniel Litt is a U of T math professor. This isn't generic AI threat talk — it's an institutional design question: when AI produces math at a few dollars per result, how do humans preserve 'understanding'. Hits all three HKR axes, but as an opinion piece rather than a product ...